A novel scalable parallel architecture for biological neural simulations

This paper presents a scalable hierarchical architecture for accelerating simulations of large-scale biological neural systems on FPGA-based platforms. The architecture provides a high degree of flexibility to optimize the parallelization ratio based on available hardware resources and model specifications such as complexity of dendritic trees. The proposed addressing scheme, design modularity and data process localization allowing the whole system to extend over multiple FPGA platforms to simulate a very large biological neural system. Compartmental approach and Hodgkin-Huxley methods are used as simulation models in our studies. The architecture is verified in MATLAB and implemented based on four types of hardware modules, with two modules synthesized on Xilinx XC5VLX110T-1 devices.